@Article{JCM-39-358,
author = {Chen , YutingCao , MingyuanYang , Yueting and Huang , Qingdao},
title = {An Adaptive Trust-Region Method for Generalized Eigenvalues of Symmetric Tensors},
journal = {Journal of Computational Mathematics},
year = {2021},
volume = {39},
number = {3},
pages = {358--374},
abstract = {<p style="text-align: justify;">For symmetric tensors, computing generalized eigenvalues is equivalent to a homogenous
polynomial optimization over the unit sphere. In this paper, we present an adaptive trust-region method for generalized eigenvalues of symmetric tensors. One of the features is
that the trust-region radius is automatically updated by the adaptive technique to improve
the algorithm performance. The other one is that a projection scheme is used to ensure
the feasibility of all iteratives. Global convergence and local quadratic convergence of
our algorithm are established, respectively. The preliminary numerical results show the
efficiency of the proposed algorithm.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.2001-m2019-0017},
url = {http://global-sci.org/intro/article_detail/jcm/18744.html}
}